Honeywell (Czechia)

Keeping up with growing electricity demand and ensuring reliable grid operation, as renewable sources reach a large proportion of generation, require end-use facilities-commercial, residential, and industrial-to be sensitive and responsive to grid connections in new ways. Automated demand response (ADR) is widely acknowledged as a key approach. The technology has progressed substantially since early implementations, with worldwide projects and a new standard. Recent applications with grid-integrated buildings and microgrids are extending the functionality, with increasing sophistication of how demand-side load profiles are managed and with integration of distributed storage and generation. This paper reviews the motivation for demand response (DR) and outlines the architectural models, technology infrastructure, and communication and control protocols that are currently in use. Four projects for commercial buildings and microgrids, in the United States, United Kingdom, and China, are described. We also point out limitations of the state of the practice that represent opportunities for research and development. Several research topics are noted, focusing on needs for modeling, optimization, and control, and some preliminary related work is discussed.

The characterization of complex air traffic situations is an important issue in air traffic management (ATM). Within the current ground-based ATM system, complexity metrics have been introduced with the goal of evaluating the difficulty experienced by air traffic controllers in guaranteeing the appropriate aircraft separation in a sector. The rapid increase in air travel demand calls for new generation ATM systems that can safely and efficiently handle higher levels of traffic. To this purpose, part of the responsibility for separation maintenance will be delegated to the aircraft, and trajectory management functions will be further automated and distributed. The evolution toward an autonomous aircraft framework envisages new tasks where assessing complexity may be valuable and requires a whole new perspective in the definition of suitable complexity metrics. This paper presents a critical analysis of the existing approaches for modeling and predicting air traffic complexity, examining their portability to autonomous ATM systems. Possible applications and related requirements will be discussed.

Many people spend most of their time in an indoor environment. A positive relationship exists between indoor environmental quality and the health, wellbeing, and productivity of occupants in buildings. The indoor environment is affected by pollutants, such as gases and particles. Pollutants can be removed from the indoor environment in various ways. Air-cleaning devices are commonly marketed as benefiting the removal of air pollutants and, consequently, improving indoor air quality. Depending on the type of cleaning technology, air cleaners may generate undesired and toxic byproducts. Different air filtration technologies, such as electrostatic precipitators (ESPs) have been introduced to the market. The ESP has been used in buildings because it can remove particles while only causing low pressure drops. Moreover, ESPs can be either in-duct or standalone units. This review aims to provide an overview of ESP use, methods for testing this product, the performance of existing ESPs concerning removing pollutants and their byproducts, and the existing market for ESPs.

Intelligent consumer energy management systems will become important elements at the delivery points of the smart grid inside homes, buildings, and industrial plants. The end users will be able to better monitor and manage their energy consumption, while utilities will gain more flexible mechanisms for management of peak demands that will extend beyond demand response initiatives as they are implemented today. With a broader use of distributed generation many buildings and campuses will become microgrids interconnecting multiple generation, storage, and consumption devices of one or several end users. We discuss how energy management and control for such facilities can be viewed as a large-scale optimization problem. Specific supply-side and demand-side aspects include on-site renewable generation, storage technologies, electric cars, dynamic pricing, and load management. Technical challenges related to the optimization formulation are noted - in general, mixed-integer, nonlinear, constrained optimization is needed. We also describe an implementation of optimization-based energy management solution for a hospital in the Netherlands, providing economic details and an analysis of the savings achieved.

The last years we are witnessing of rapid advances in the industrial automation domain, mainly driven by business needs towards agility and supported by new disruptive technologies. Future factories will rely on multi-system interactions and collaborative cross-layer management and automation approaches. Such a factory, configured and managed from architectural and behavioural viewpoints, under the service-oriented architecture (SOA) paradigm is virtualized by services exposed by its key components (both HW and SW). One of the main results of this virtualization is that the factory is transformed into a “cloud of services”, where dynamic resource allocation and interactions take place. This paper presents a view on such architecture, its specification, the main motivation and considerations, as well as the preliminary services it may need to support.

The development of applications for high-performance embedded systems is typically a long and error-prone process. In addition to the required functions, developers must consider various and often conflicting non-functional application requirements such as performance and energy efficiency. The complexity of this process is exacerbated by the multitude of target architectures and the associated retargetable mapping tools. This paper introduces an As-pect-Oriented Programming (AOP) approach that conveys domain knowledge and non-functional requirements to optimizers and mapping tools. We describe a novel AOP language, LARA, which allows the specification of compi-lation strategies to enable efficient generation of software code and hardware cores for alternative target architectures. We illustrate the use of LARA for code instrumentation and analysis, and for guiding the application of compiler and hardware synthesis optimizations. An important LARA feature is its capability to deal with different join points, action models, and attributes, and to generate an aspect intermediate representation. We present examples of our aspect-oriented hardware/software design flow for mapping real-life application codes to embedded platforms based on Field Programmable Gate Array (FPGA) technology.

Condensing boilers achieve higher efficiency than traditional boilers by using waste heat in flue gases to preheat cold return water entering the boiler. Water vapor produced during combustion is condensed into liquid form, thus recovering its latent heat of vaporization, leading to around 10–12% increased efficiency. Many countries have encouraged the use of condensing boilers with financial incentives. It is thus important to develop software tools to assess the correct functioning of the boiler and eventually detect problems. Current monitoring tools are based on boiler static maps and on large sets of historical data, and are unable to assess timely loss of performance due to degradation of the efficiency curve or water leakages. This work develops a set of fault detection and diagnosis tools for dynamic energy efficiency monitoring and assessment in condensing boilers, i.e. performance degradation and faults can be detected using real-time measurements: this real-time feature is particularly relevant because of the limited amount of data that can be stored by state-of-the-art building energy management systems. The monitoring tools are organized as follows: a bimodal parameter estimator to detect deviations of the efficiency of the boiler from nominal values in both condensing and noncondensing mode; a virtual sensor for the estimation of the water mass flow rate; filters to detect actuator and sensor faults, possibly due to control and sensing problems. Most importantly, structural properties for detection and isolation of actuators and sensing faults are given: these properties are crucial to understand which faults can be diagnosed given the available measurements. The effectiveness of these tools is verified via extensive simulations.

Estimation of energy models from data is an important part of advanced fault detection and diagnosis tools for smart energy purposes. Estimated energy models can be used for a large variety of management and control tasks, spanning from model predictive building control to estimation of energy consumption and user behavior. In practical implementation, problems to be considered are the fact that some measurements of relevance are missing and must be estimated, and the fact that other measurements, collected at low sampling rate to save memory, make discretization of physics-based models critical. These problems make classical estimation tools inadequate and call for appropriate dual estimation schemes where states and parameters of a system are estimated simultaneously. In this work we develop dual estimation schemes based on Extended Kalman Filtering (EKF) and Unscented Kalman Filtering (UKF) for constructing building energy models from data: in order to cope with the low sampling rate of data (with sampling time 15 min), an implicit discretization (Euler backward method) is adopted to discretize the continuous-time heat transfer dynamics. It is shown that explicit discretization methods like the Euler forward method, combined with 15 min sampling time, are ineffective for building reliable energy models (the discrete-time dynamics do not match the continuous-time ones): even explicit methods of higher order like the Runge–Kutta method fail to provide a good approximation of the continuous-time dynamics which such large sampling time. Either smaller time steps or alternative discretization methods are required. We verify that the implicit Euler backward method provides good approximation of the continuous-time dynamics and can be easily implemented for our dual estimation purposes. The applicability of the proposed method in terms of estimation of both states and parameters is demonstrated via simulations and using historical data from a real-life building.

This paper is devoted to the terrain aided navigation (TAN) systems based on the state estimation algorithms. In particular, the emphasis is laid on the design of the Bayesian Rao–Blackwellized point-mass filter for nonlinear state-space models of a specific structure typically used in the terrain aided navigation. The proposed filter preserves advantages of the point-mass filter, including the high estimation accuracy, robust initialization, deterministic nature of the algorithm, and predictable computational complexity, while the computational complexity is significantly reduced. The performance of the Rao–Blackwellized point-mass filter is illustrated using a simulated numerical example and a TAN system based on the real data.

Engineers who design hard real-time embedded systems express a need for several times the performance available today while keeping safety as major criterion. A breakthrough in performance is expected by parallelizing hard real-time applications and running them on an embedded multi-core processor, which enables combining the requirements for high-performance with timing-predictable execution. parMERASA will provide a timing analyzable system of parallel hard real-time applications running on a scalable multicore processor. parMERASA goes one step beyond mixed criticality demands: It targets future complex control algorithms by parallelizing hard real-time programs to run on predictable multi-/many-core processors. We aim to achieve a breakthrough in techniques for parallelization of industrial hard real-time programs, provide hard real-time support in system software, WCET analysis and verification tools for multi-cores, and techniques for predictable multi-core designs with up to 64 cores.

Microgrids (MGs) are small-scale local energy grids. While dedicated to cover local power needs, their structure and operation is usually quite complex. Complexity arises due to a number of factors: in the first instance, a variety of operational modes - among them, MGs can be considered to be operated autonomously whenever the main distribution grid is not available; furthermore, the heterogeneity of energy types in a MG - not exclusively electrical energy, but also thermal for instance; also, the different functions that a MG energy management system has to fulfill - like coordination and dispatching of multiple generation, transfer, transformation and storage devices; finally, the external and internal random factors that affect operations. All these aspects make control and scheduling of a MG quite a challenging task. On the other hand, this widespread complexity leaves much room for improvement on the current state of the art. An advancement on the state of the art requires the development of a realistic model of the system at hand. This work puts forward a model of a MG that is based on the framework of Stochastic Hybrid Systems (SHS). SHS models can capture the interaction between probabilistic elements and discrete and continuous dynamics, and thus promise to be able to tame the complexity of the systems discussed above. This work displays the outcomes of model simulations and discusses potential development of general analysis and synthesis approaches over SHS models (e.g., based on model checking and on approximate dynamic programming) for typical challenges in MGs.

Microgrid energy management stands for challenging optimization problem where continuous (economic dispatch) and discrete optimization (unit commitment) tasks are solved. Often Microgrid optimization leads to complex problem where optimization methods usually meet curse of dimensionality. We adopt approximate dynamic programming (ADP) as the promising optimization technique which can overcome curse of dimensionality. In this paper, energy management system based on ADP is introduced and its behavior is demonstrated on small scale Microgrid which is connected to distribution network and includes wind turbine, chiller plant, thermal storage and cooling load. The paper describes policy search approach to ADP and selected approximation architectures in the context of energy optimization. The ADP results are compared with the results of the solution based on dynamic programming approach.

Abstract An active technique for friction drag reduction in a turbulent channel flow is studied by direct numerical simulations. The flow modification is induced by the steady rotation of rigid flush-mounted discs, located next to one another on the walls. The effect of the disc motion on the turbulent drag is investigated at a Reynolds number of ${R}_{\tau } = 180$ , based on the friction velocity of the stationary-wall case and the half channel height. For a fixed maximum disc tip velocity, drag reduction can be achieved when the disc diameter is larger than a threshold, while below this threshold the drag increases. A maximum drag reduction of 23% is computed. The net power saved, obtained by taking into account the power spent to enforce the rotational motion against the fluid viscous resistance, is found to be positive and reach 10%. The disc-flow parameters required for commercial aircraft flight conditions and flows over high-speed trains and ship hulls are estimated and future implementations based on existing micro-electromagnetic motor and micro-air turbine technologies are discussed.

It has been shown that an increased mental workload in pilots could lead to a decrease in their situation awareness, which could lead, in turn, to a worse piloting performance and ultimately to critical human errors. Assessing the current pilot's psycho-physiological state is a hot topic of interest for developing advanced embedded cockpits systems capable of adapting their behavior to the state and performance of the pilot. In this work, we investigate a method to classify different levels of cognitive workload starting from synchronized EEG and eye-tracking information. The classifier object of the research is targeted to score a performance high enough to be applicable as a gauge for performance of unobtrusive monitoring systems working with data of lower quality.

In this paper, we propose a generic notion of distance between systems that can be used to measure discrepancy between open-loop systems in a feedback sense under several uncertainty structures. When the uncertainty structure is chosen to be four-block (or equivalently, normalized coprime factor) uncertainty, then this generic distance measure reduces to the well-known nu-gap metric. Associated with this generic distance notion, we also define a generic stability margin notion that allows us to give the distance measure a feedback interpretation by deriving generic robust stability and robust performance results. The proposed distance notion and the corresponding results exploit a powerful generalization of the small-gain theorem which handles perturbations in Rfr <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> , rather than only in Rfr <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> . When the uncertainty structure is fixed to one of the standard structures (e.g., additive, multiplicative, inverse multiplicative, coprime factor, four-block or any mixtures of the above), we give a step-by-step procedure (based on model validation ideas) that shows how the generic notion of distance and the correspondingly generic winding number conditions can be reduced to simple formulae. This work provides a unified framework that captures and embeds previous results in this area and also completes the picture by showing how other results of a similar nature can be obtained from the same framework. The techniques used involve only basic linear algebra, so they also provide a simplification of previous advanced proofs. Furthermore, the various distance measures so created can be used for non-conservative model embedding into the smallest uncertain family. An illustrative example is also given that demonstrates the superior qualities, above the nu-gap metric, of a particular distance measure obtained from this work in situations where the plant is lightly-damped. All systems considered in this paper are linear time-invariant.

In a SOA-based system the applications are organized in a manner such that interoperable services can be used from different domains. In a process industry context, different domains can refer to, for example, process instrumentation and monitoring, execution of process control, data acquisition, etc. Large process industry systems are a complex and potentially very large sets of multi-disciplinary, heterogeneous, networked distributed systems. Current industrial process control systems are typically vendor specific; in addition the different domains are associated with different layers, different standards and different technologies. In the paper the authors report about the investigations and assessments performed to find answers for four major critical questions that arise as key when technologies have to be selected and used in a true Service Oriented Architecture (SOA) based distributed large scale Process Monitoring and Control system: (1) Real-time SOA (what are the limits of bringing SOA into high performance control loops?); (2) Management of large scale industrial distributed control systems (is it feasible to manage up to tens of thousands of service-oriented devices?); (3) Distributed event-based systems are asynchronous (what are the limits compared to traditional periodic scanning systems?) and (4) Service specification (which semantics are the most suitable for specifying process control and monitoring services?).

Plasma‐polymerized films (interlayers) of tetravinylsilane in mixture with oxygen gas (oxygen fraction 0‐0.71) were coated on glass fibers (GF) used as reinforcements in GF/polyester composite. Oxygen atoms of increased concentration (0‐18 at.%) were partly incorporated into the plasma polymer network, forming SiOC/COC bonding species and partly forming side polar (hydroxyl, carbonyl) groups with enhanced oxygen fraction. The amount of oxygen in plasma coatings influenced the Young's modulus, interfacial adhesion, and surface free energy of the interlayer. To determine the interfacial shear strength, a microindentation test was implemented for individual glass fibers on a cross‐section of GF/polyester composite. The interfacial shear strength for oxidized plasma coatings was up to 21% higher than that for the non‐oxidized interlayer, indicating a direct chemical effect of oxygen atoms on interphase properties. The interphase shear failure was controlled by the shear strength at the interlayer/fiber interface as follows from experimental and model data. POLYM. COMPOS., 40:E186–E193, 2019. © 2017 Society of Plastics Engineers

Summary The development of applications for high‐performance embedded systems is a long and error‐prone process because in addition to the required functionality, developers must consider various and often conflicting nonfunctional requirements such as performance and/or energy efficiency. The complexity of this process is further exacerbated by the multitude of target architectures and mapping tools. This article describes LARA, an aspect‐oriented programming language that allows programmers to convey domain‐specific knowledge and nonfunctional requirements to a toolchain composed of source‐to‐source transformers, compiler optimizers, and mapping/synthesis tools. LARA is sufficiently flexible to target different tools and host languages while also allowing the specification of compilation strategies to enable efficient generation of software code and hardware cores (using hardware description languages) for hybrid target architectures – a unique feature to the best of our knowledge not found in any other aspect‐oriented programming language. A key feature of LARA is its ability to deal with different models of join points, actions, and attributes. In this article, we describe the LARA approach and evaluate its impact on code instrumentation and analysis and on selecting critical code sections to be migrated to hardware accelerators for two embedded applications from industry. Copyright © 2014 John Wiley &amp; Sons, Ltd.

This paper deals with finite-dimensional boundary control of the two-dimensional (2-D) flow between two infinite parallel planes. Surface transpiration along a few regularly spaced sections of the bottom wall is used to control the flow. Measurements from several discrete, suitably placed shear-stress sensors provide the feedback. Unlike other studies in this area, the flow is not assumed to be periodic, and spatially growing flows are considered. Using spatial discretization in the streamwise direction, frequency responses for a relevant part of the channel are obtained. A low-order model is fitted to these data and the modeling uncertainty is estimated. An H/sub /spl infin// controller is designed to guarantee stability for the model set and to reduce the wall-shear stress at the channel wall. A nonlinear Navier-Stokes PDE solver was used to test the designs in the loop. The only assumption made in these simulations is that the flow is two dimensional. The results showed that, although the problem was linearized when designing the controller, the controller could significantly reduce fundamental 2-D disturbances in practice.

This letter introduces a new switched adaptive control mechanism that can cope with parametric uncertainty while using discrete and saturated actuators. Control of air handling units (AHUs), where air and water supply have discrete and saturated characteristics, is the motivational drive behind this letter. We show that the cheap actuation and low computational requirements of building automation installations can be met after recasting the AHU thermal dynamics as a switched linear system with discrete working modes. Adaptive laws with antiwindup compensation and a switching law based on dwell time are introduced to cope with the uncertainties and input constraints of the switched linear system. Tracking performance is shown analytically and demonstrated via a numerical test case.